CAlphabet | The class Alphabet implements an alphabet and alphabet utility functions |
CArray< T > | Template class Array implements a dense one dimensional array |
CArray2< T > | Template class Array2 implements a dense two dimensional array |
CArray3< T > | Template class Array3 implements a dense three dimensional array |
CAsciiFile | A Ascii File access class |
CAttributeFeatures | Implements attributed features, that is in the simplest case a number of (attribute, value) pairs |
CAUCKernel | The AUC kernel can be used to maximize the area under the receiver operator characteristic curve (AUC) instead of margin in SVM training |
CAvgDiagKernelNormalizer | Normalize the kernel by either a constant or the average value of the diagonal elements (depending on argument c of the constructor) |
CBinaryFile | A Binary file access class |
CBinaryStream< T > | Memory mapped emulation via binary streams (files) |
CBitString | String class embedding a string in a compact bit representation |
CBrayCurtisDistance | Class Bray-Curtis distance |
CCache< T > | Template class Cache implements a simple cache |
CCanberraMetric | Class CanberraMetric |
CCanberraWordDistance | Class CanberraWordDistance |
CChebyshewMetric | Class ChebyshewMetric |
CChi2Kernel | The Chi2 kernel operating on realvalued vectors computes the chi-squared distance between sets of histograms |
CChiSquareDistance | Class ChiSquareDistance |
CClassifier | A generic classifier interface |
CCombinedDotFeatures | Features that allow stacking of a number of DotFeatures |
CCombinedFeatures | The class CombinedFeatures is used to combine a number of of feature objects into a single CombinedFeatures object |
CCombinedKernel | The Combined kernel is used to combine a number of kernels into a single CombinedKernel object by linear combination |
CCommUlongStringKernel | The CommUlongString kernel may be used to compute the spectrum kernel from strings that have been mapped into unsigned 64bit integers |
CCommWordStringKernel | The CommWordString kernel may be used to compute the spectrum kernel from strings that have been mapped into unsigned 16bit integers |
CCompressor | |
CConstKernel | The Constant Kernel returns a constant for all elements |
CCosineDistance | Class CosineDistance |
CCplex | |
CCPLEXSVM | |
CCustomDistance | The Custom Distance allows for custom user provided distance matrices |
CCustomKernel | The Custom Kernel allows for custom user provided kernel matrices |
CDecompressString< ST > | Preprocessor that decompresses compressed strings |
CDiagKernel | The Diagonal Kernel returns a constant for the diagonal and zero otherwise |
CDiceKernelNormalizer | DiceKernelNormalizer performs kernel normalization inspired by the Dice coefficient (see http://en.wikipedia.org/wiki/Dice's_coefficient) |
CDistance | Class Distance |
CDistanceKernel | The Distance kernel takes a distance as input |
CDistanceMachine | A generic DistanceMachine interface |
CDistribution | Base class Distribution from which all methods implementing a distribution are derived |
CDomainAdaptationSVM | Class DomainAdaptiveSVM |
CDomainAdaptationSVMLinear | Class DomainAdaptiveSVMLinear |
CDotFeatures | Features that support dot products among other operations |
CDotKernel | Template class DotKernel is the base class for kernels working on DotFeatures |
CDummyFeatures | The class DummyFeatures implements features that only know the number of feature objects (but don't actually contain any) |
CDynamicArray< T > | Template Dynamic array class that creates an array that can be used like a list or an array |
CDynamicArrayPtr | Template Dynamic array class that creates an array that can be used like a list or an array |
CDynInt< T, sz > | Integer type of dynamic size |
CDynProg | Dynamic Programming Class |
CEuclidianDistance | Class EuclidianDistance |
CExplicitSpecFeatures | Features that compute the Spectrum Kernel feature space explicitly |
CFeatures | The class Features is the base class of all feature objects |
CFile | A File access base class |
CFirstElementKernelNormalizer | Normalize the kernel by a constant obtained from the first element of the kernel matrix, i.e. |
CFixedDegreeStringKernel | The FixedDegree String kernel takes as input two strings of same size and counts the number of matches of length d |
CFKFeatures | The class FKFeatures implements Fischer kernel features obtained from two Hidden Markov models |
CGaussianKernel | The well known Gaussian kernel (swiss army knife for SVMs) computed on CDotFeatures |
CGaussianMatchStringKernel | The class GaussianMatchStringKernel computes a variant of the Gaussian kernel on strings of same length |
CGaussianShiftKernel | An experimental kernel inspired by the WeightedDegreePositionStringKernel and the Gaussian kernel |
CGaussianShortRealKernel | The well known Gaussian kernel (swiss army knife for SVMs) on dense short-real valued features |
CGCArray< T > | |
CGeodesicMetric | Class GeodesicMetric |
CGHMM | Class GHMM - this class is non-functional and was meant to implement a Generalize Hidden Markov Model (aka Semi Hidden Markov HMM) |
CGMNPLib | Class GMNPLib Library of solvers for Generalized Minimal Norm Problem (GMNP) |
CGMNPSVM | Class GMNPSVM implements a one vs. rest MultiClass SVM |
CGNPPLib | Class GNPPLib, a Library of solvers for Generalized Nearest Point Problem (GNPP) |
CGNPPSVM | Class GNPPSVM |
CGPBTSVM | Class GPBTSVM |
CHammingWordDistance | Class HammingWordDistance |
CHash | Collection of Hashing Functions |
CHashedWDFeatures | Features that compute the Weighted Degreee Kernel feature space explicitly |
CHashedWDFeaturesTransposed | Features that compute the Weighted Degreee Kernel feature space explicitly |
CHierarchical | Agglomerative hierarchical single linkage clustering |
CHistogram | Class Histogram computes a histogram over all 16bit unsigned integers in the features |
CHistogramIntersectionKernel | The HistogramIntersection kernel operating on realvalued vectors computes the histogram intersection distance between sets of histograms. Note: the current implementation assumes positive values for the histograms, and input vectors should sum to 1 |
CHistogramWordStringKernel | The HistogramWordString computes the TOP kernel on inhomogeneous Markov Chains |
CHMM | Hidden Markov Model |
CIdentityKernelNormalizer | Identity Kernel Normalization, i.e. no normalization is applied |
CImplicitWeightedSpecFeatures | Features that compute the Weighted Spectrum Kernel feature space explicitly |
CIndirectObject< T, P > | Array class that accesses elements indirectly via an index array |
CIntronList | Class IntronList |
CJensenMetric | Class JensenMetric |
CKernel | The Kernel base class |
CKernelDistance | The Kernel distance takes a distance as input |
CKernelMachine | A generic KernelMachine interface |
CKernelNormalizer | The class Kernel Normalizer defines a function to post-process kernel values |
CKernelPerceptron | Class KernelPerceptron - currently unfinished implementation of a Kernel Perceptron |
CKMeans | KMeans clustering, partitions the data into k (a-priori specified) clusters |
CKNN | Class KNN, an implementation of the standard k-nearest neigbor classifier |
CKRR | |
CLabels | The class Labels models labels, i.e. class assignments of objects |
CLaRank | |
CLBPPyrDotFeatures | Implement DotFeatures for the polynomial kernel |
CLDA | |
CLibLinear | Class to implement LibLinear |
CLibSVM | LibSVM |
CLibSVMMultiClass | Class LibSVMMultiClass |
CLibSVMOneClass | Class LibSVMOneClass |
CLibSVR | Class LibSVR, performs support vector regression using LibSVM |
CLinearClassifier | Class LinearClassifier is a generic interface for all kinds of linear classifiers |
CLinearHMM | The class LinearHMM is for learning Higher Order Markov chains |
CLinearKernel | Computes the standard linear kernel on CDotFeatures |
CLinearStringKernel | Computes the standard linear kernel on dense char valued features |
CList | Class List implements a doubly connected list for low-level-objects |
CListElement | Class ListElement, defines how an element of the the list looks like |
CLocalAlignmentStringKernel | The LocalAlignmentString kernel compares two sequences through all possible local alignments between the two sequences |
CLocalityImprovedStringKernel | The LocalityImprovedString kernel is inspired by the polynomial kernel. Comparing neighboring characters it puts emphasize on local features |
CLogPlusOne | Preprocessor LogPlusOne does what the name says, it adds one to a dense real valued vector and takes the logarithm of each component of it |
CLPBoost | |
CLPM | |
CManhattanMetric | Class ManhattanMetric |
CManhattanWordDistance | Class ManhattanWordDistance |
CMatchWordStringKernel | The class MatchWordStringKernel computes a variant of the polynomial kernel on strings of same length converted to a word alphabet |
CMath | Class which collects generic mathematical functions |
CMemoryMappedFile< T > | Memory mapped file |
CMinkowskiMetric | Class MinkowskiMetric |
CMKL | Multiple Kernel Learning |
CMKLClassification | Multiple Kernel Learning for two-class-classification |
CMKLMultiClass | MKLMultiClass is a class for L1-norm multiclass MKL |
CMKLOneClass | Multiple Kernel Learning for one-class-classification |
CMKLRegression | Multiple Kernel Learning for regression |
CMPDSVM | Class MPDSVM |
CMultiClassSVM | Class MultiClassSVM |
CMultitaskKernelMaskNormalizer | The MultitaskKernel allows Multitask Learning via a modified kernel function |
CMultitaskKernelMaskPairNormalizer | The MultitaskKernel allows Multitask Learning via a modified kernel function |
CMultitaskKernelMklNormalizer | Base-class for parameterized Kernel Normalizers |
CMultitaskKernelNormalizer | The MultitaskKernel allows Multitask Learning via a modified kernel function |
CMultitaskKernelPlifNormalizer | The MultitaskKernel allows learning a piece-wise linear function (PLIF) via MKL |
CMultitaskKernelTreeNormalizer | The MultitaskKernel allows Multitask Learning via a modified kernel function based on taxonomy |
CNode | A CNode is an element of a CTaxonomy, which is used to describe hierarchical structure between tasks |
CNormDerivativeLem3 | Preprocessor NormDerivativeLem3, performs the normalization used in Lemma3 in Jaakola Hausslers Fischer Kernel paper currently not implemented |
CNormOne | Preprocessor NormOne, normalizes vectors to have norm 1 |
COligoStringKernel | This class offers access to the Oligo Kernel introduced by Meinicke et al. in 2004 |
CCombinedDotFeatures::combined_feature_iterator | |
CPCACut | |
CPerceptron | Class Perceptron implements the standard linear (online) perceptron |
CPerformanceMeasures | Class to implement various performance measures |
CPlif | Class Plif |
CPlifArray | Class PlifArray |
CPlifBase | Class PlifBase |
CPlifMatrix | Store plif arrays for all transitions in the model |
CPluginEstimate | Class PluginEstimate |
CPolyFeatures | Implement DotFeatures for the polynomial kernel |
CPolyKernel | Computes the standard polynomial kernel on CDotFeatures |
CPolyMatchStringKernel | The class PolyMatchStringKernel computes a variant of the polynomial kernel on strings of same length |
CPolyMatchWordStringKernel | The class PolyMatchWordStringKernel computes a variant of the polynomial kernel on word-features |
CPreProc | Class PreProc defines a preprocessor interface |
CPruneVarSubMean | Preprocessor PruneVarSubMean will substract the mean and remove features that have zero variance |
CPyramidChi2 | Pyramid Kernel over Chi2 matched histograms |
CQPBSVMLib | Class QPBSVMLib |
CRealDistance | Class RealDistance |
CRealFileFeatures | The class RealFileFeatures implements a dense double-precision floating point matrix from a file |
CRegulatoryModulesStringKernel | The Regulaty Modules kernel, based on the WD kernel, as published in Schultheiss et al., Bioinformatics (2009) on regulatory sequences |
CRidgeKernelNormalizer | Normalize the kernel by adding a constant term to its diagonal. This aids kernels to become positive definite (even though they are not - often caused by numerical problems) |
CSalzbergWordStringKernel | The SalzbergWordString kernel implements the Salzberg kernel |
CScatterKernelNormalizer | |
CScatterSVM | ScatterSVM - Multiclass SVM |
CSegmentLoss | Class IntronList |
CSerializableAsciiFile | |
CSerializableFile | |
CSet< T > | Template Set class |
CSGObject | Class SGObject is the base class of all shogun objects |
CSigmoidKernel | The standard Sigmoid kernel computed on dense real valued features |
CSignal | Class Signal implements signal handling to e.g. allow ctrl+c to cancel a long running process |
CSignalModel | Class SignalModel |
CSimpleDistance< ST > | Template class SimpleDistance |
CSimpleFeatures< ST > | The class SimpleFeatures implements dense feature matrices |
CSimpleFile< T > | Template class SimpleFile to read and write from files |
CSimpleLocalityImprovedStringKernel | SimpleLocalityImprovedString kernel, is a ``simplified'' and better performing version of the Locality improved kernel |
CSimplePreProc< ST > | Template class SimplePreProc, base class for preprocessors (cf. CPreProc) that apply to CSimpleFeatures (i.e. rectangular dense matrices) |
CSNPFeatures | Features that compute the Weighted Degreee Kernel feature space explicitly |
CSNPStringKernel | The class SNPStringKernel computes a variant of the polynomial kernel on strings of same length |
CSortUlongString | Preprocessor SortUlongString, sorts the indivual strings in ascending order |
CSortWordString | Preprocessor SortWordString, sorts the indivual strings in ascending order |
CSparseDistance< ST > | Template class SparseDistance |
CSparseEuclidianDistance | Class SparseEucldianDistance |
CSparseFeatures< ST > | Template class SparseFeatures implements sparse matrices |
CSparseKernel< ST > | Template class SparseKernel, is the base class of kernels working on sparse features |
CSparsePolyFeatures | Implement DotFeatures for the polynomial kernel |
CSparsePreProc< ST > | Template class SparsePreProc, base class for preprocessors (cf. CPreProc) that apply to CSparseFeatures |
CSparseSpatialSampleStringKernel | Sparse Spatial Sample String Kernel by Pavel Kuksa <pkuksa@cs.rutgers.edu> and Vladimir Pavlovic <vladimir@cs.rutgers.edu> |
CSpectrumMismatchRBFKernel | |
CSpectrumRBFKernel | |
CSqrtDiagKernelNormalizer | SqrtDiagKernelNormalizer divides by the Square Root of the product of the diagonal elements |
CStringDistance< ST > | Template class StringDistance |
CStringFeatures< ST > | Template class StringFeatures implements a list of strings |
CStringFileFeatures< ST > | File based string features |
CStringKernel< ST > | Template class StringKernel, is the base class of all String Kernels |
CStringPreProc< ST > | Template class StringPreProc, base class for preprocessors (cf. CPreProc) that apply to CStringFeatures (i.e. strings of variable length) |
CSubGradientLPM | |
CSubGradientSVM | Class SubGradientSVM |
CSVM | A generic Support Vector Machine Interface |
CSVMLight | |
CSVMLightOneClass | |
CSVMLin | Class SVMLin |
CSVMOcas | Class SVMOcas |
CSVMSGD | Class SVMSGD |
CSVRLight | |
CTanimotoDistance | Class Tanimoto coefficient |
CTanimotoKernelNormalizer | TanimotoKernelNormalizer performs kernel normalization inspired by the Tanimoto coefficient (see http://en.wikipedia.org/wiki/Jaccard_index ) |
CTaxonomy | CTaxonomy is used to describe hierarchical structure between tasks |
CTensorProductPairKernel | Computes the Tensor Product Pair Kernel (TPPK) |
CTime | Class Time that implements a stopwatch based on either cpu time or wall clock time |
CTOPFeatures | The class TOPFeatures implements TOP kernel features obtained from two Hidden Markov models |
CTrainPredMaster | |
CTrie< Trie > | |
CTron | |
CVarianceKernelNormalizer | VarianceKernelNormalizer divides by the ``variance'' |
CWDFeatures | Features that compute the Weighted Degreee Kernel feature space explicitly |
CWDSVMOcas | Class WDSVMOcas |
CWeightedCommWordStringKernel | The WeightedCommWordString kernel may be used to compute the weighted spectrum kernel (i.e. a spectrum kernel for 1 to K-mers, where each k-mer length is weighted by some coefficient ) from strings that have been mapped into unsigned 16bit integers |
CWeightedDegreePositionStringKernel | The Weighted Degree Position String kernel (Weighted Degree kernel with shifts) |
CWeightedDegreeRBFKernel | |
CWeightedDegreeStringKernel | The Weighted Degree String kernel |
CZeroMeanCenterKernelNormalizer | ZeroMeanCenterKernelNormalizer centers the kernel in feature space |
DynArray< T > | Template Dynamic array class that creates an array that can be used like a list or an array |
CExplicitSpecFeatures::explicit_spec_feature_iterator | |
CHashedWDFeatures::hashed_wd_feature_iterator | |
CHashedWDFeaturesTransposed::hashed_wd_transposed_feature_iterator | |
IO | Class IO, used to do input output operations throughout shogun |
joint_list_struct | |
K_THREAD_PARAM< T > | |
libqp_state_T | |
MKLMultiClassGLPK | MKLMultiClassGLPK is a helper class for MKLMultiClass |
MKLMultiClassGradient | MKLMultiClassGradient is a helper class for MKLMultiClass |
MKLMultiClassOptimizationBase | MKLMultiClassOptimizationBase is a helper class for MKLMultiClass |
Model | Class Model |
Parallel | Class Parallel provides helper functions for multithreading |
Parameter | |
CPolyFeatures::poly_feature_iterator | |
segment_loss_struct | Segment loss |
SerializableAsciiReader00 | |
ShogunException | Class ShogunException defines an exception which is thrown whenever an error inside of shogun occurs |
CSimpleFeatures< ST >::simple_feature_iterator | |
CSparseFeatures< ST >::sparse_feature_iterator | |
CSparsePolyFeatures::sparse_poly_feature_iterator | |
SSKDoubleFeature | |
SSKFeatures | |
SSKTripleFeature | |
TParameter | |
CSerializableFile::TSerializableReader | |
TSGDataType | |
TSparse< T > | |
TSparseEntry< T > | |
TString< T > | |
Version | Class Version provides version information |
CWDFeatures::wd_feature_iterator | |
CImplicitWeightedSpecFeatures::wspec_feature_iterator | |